KSC 2019
Current Result Document :
| 한글제목(Korean Title) |
SeHAGAN: GAN을 이용한 순차적 인간행동 생성 |
| 영문제목(English Title) |
SeHAGAN: Sequential Human Actions Generation with GANs |
| 저자(Author) |
아지즈 시야에프
조근식
AzizSiyaev
Geun-Sik Jo
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| 원문수록처(Citation) |
VOL 46 NO. 02 PP. 0500 ~ 0502 (2019. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The Generative Adversarial Networks (GANs) have shown rapid development in different content-creation tasks. Among Them, the video generation gets its own attention due ti the development of various human centric applications like avatar animation. In this paper, we proposed a method to generate sequential human actions using a two-stage GANs pipeline. First, we produce pose skeleton with our Poses Generator, and then we textured them with a Frame Generator. Results showed that the proposed method SeHAGAN generates a plausible and high-quality video of human movements
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| 키워드(Keyword) |
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| 파일첨부 |
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